# Tag Info

The notation $\|x_n - \mu\|^2$ stands for the squared $L_2$ norm of the vector $x_n - \mu$. The squared $L_2$ norm of the $d$-dimensional vector $v = \begin{pmatrix} v_1 & \cdots & v_d \end{pmatrix}$ is $$\|v\|^2 = v_1^2 + \cdots + v_d^2.$$ In particular, when $d = 1$, we have $\|v\|^2 = v_1^2$. The formula you state works for the Gaussian ...